Correlation of the detection rate of upper GI cancer with artificial intelligence score: results from a multicenter trial (with video).

BACKGROUND AND AIMS: The quality of EGD is a prerequisite for a high detection rate of upper GI lesions, especially early gastric cancer. Our previous study showed that an artificial intelligence system, named intelligent detection endoscopic assistant (IDEA), could help to monitor blind spots and provide an operation score during EGD. Here, we verified the effectiveness of IDEA to help evaluate the quality of EGD in a large-scale multicenter trial. METHODS: Patients undergoing EGD in 12 hospitals were consecutively enrolled. All hospitals were equipped with IDEA developed using deep convolutional neural networks and long short-term memory. Patients were examined by EGD, and the results were recorded by IDEA. The primary outcome was the detection rate of upper GI cancer. Secondary outcomes were part scores, total scores, and endoscopic procedure time, which were analyzed by IDEA. RESULTS: A total of 17,787 patients were recruited. The total detection rate of cancer-positive cases was 1.50%, ranging from .60% to 3.94% in each hospital. The total detection rate of early cancer-positive cases was .36%, ranging from .00% to 1.58% in each hospital. The average total score analyzed by IDEA ranged from 64.87 ± 16.87 to 83.50 ± 9.57 in each hospital. The cancer detection rate in each hospital was positively correlated with total score (r = .775, P = .003). Similarly, the early cancer detection rate was positively correlated with total score (r = .756, P = .004). CONCLUSIONS: This multicenter trial confirmed that the quality of the EGD result is positively correlated with the detection rate of cancer, which can be monitored by IDEA. (Clinical trial registration number: ChiCTR2000029001.).

Metabolomics analysis and rapid identification of changes in chemical ingredients in crude and processed Astragali Radix by UPLC-QTOF-MS combined with novel informatics UNIFI platform.

Astragali Radix, the root of Astragalus membranaceus (Fisch.) Bge. var. mongholicus (Bge.) Hsiao or Astragalus membranaceus (Fisch.) Bge., is widely used as a tonic decoction pieces in the clinic of traditional Chinese medicine (TCM). Astragali Radix has various processed products with varying pharmacological actions. There is no modern scientific evidence to explain the differences in pharmacological activities and related mechanisms. In the present study, we explore the changes in chemical components in Astragali Radix after processing, by ultra-high performance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS) combined with novel informatics UNIFI platform and multivariate statistical analysis. Our results showed that the crude and various processed products could be clearly separated in PCA scores plot and 15 significant markers could be used to distinguish crude and various processed products by OPLS-DA in UNIFI platform. In conclusion, the present study provided a basis of chemical components for revealing connotation of different processing techniques on Astragali Radix.